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This study developed a leakage-resistant machine learning framework for classifying hair loss types, emphasizing transparency through explainable AI. Among tested models, Extreme Gradient Boosting excelled, achieving high accuracy and stability on both cross-validation and holdout datasets.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.

This study found that an ML model incorporating factors like Breslow thickness and age improved cutaneous malignant melanoma prognosis predictions compared to TNM staging, with a C-index of 80% versus 66.6% for TNM alone, suggesting ML's potential for personalized prognostication.

This study found that a data-driven model using XGBoost effectively predicts individualized responses to minoxidil for androgenetic alopecia, outperforming traditional methods in accuracy and reliability.

This study reported that AutoML effectively modeled itching and pain development, as well as app use, in patients with chronic eczema or psoriasis using a smartphone monitoring app, revealing that factors like BMI, age, and disease activity significantly influenced app engagement.

This study found that using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.

This study found that among various predictive models for baldness risk, Random Forest Regression performed best with the lowest mean squared error and highest R², indicating strong predictive accuracy, especially with complex datasets, while Linear Regression was better suited to simpler datasets.

This review discusses the roles of immune responses and cells in skin health and disease, emphasizing recent insights into their mechanisms and potential therapeutic applications, but presents no new experimental results.

This review discusses alopecia, its pathophysiology, nanotechnology-based drug delivery systems, and related quality of life issues, without reporting new clinical results.

In this study, researchers found that microRNAs and specific target genes, such as MiR-195 and genes like CHP1, SMAD2, FZD6, and SIAH1, play significant roles in regulating hair follicle initiation in cashmere goats.

This study found that metabolism-related genes significantly impact the prognosis and metastasis in breast cancer, and the development of prediction models may guide personalized therapeutic strategies.

This study observed that using the Fisher score feature selection approach with capsule network models led to a promising 94% accuracy in diabetes detection, indicating its potential as a diagnostic tool.

This review discusses existing forensic DNA phenotyping panels for biogeographical ancestry and externally visible characteristics and highlights major technical limitations, including terminology issues, genetic knowledge gaps, and technological debates; it reports no new results.

This study reviews the transformative role of artificial intelligence in biomaterial design, highlighting its ability to reduce costs through virtual screening, enhance material performance, and predict biological interactions to advance personalized and precision medicine.

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.

This study found that faster algorithms for inferring ancestry in genomic data can better capture historical and functional insights into genome variation than traditional methods in large datasets like the UK Biobank.

In this study, researchers used EHR data from two large PCORnet networks to identify a range of long COVID diagnoses, highlighting varying post-acute risks across populations in NYC and Florida.

In this study, researchers introduced a machine learning approach to discover new nanozymes through the DiZyme platform, enabling the accurate prediction of multiple catalytic activities, and providing a comprehensive database and assistant resources for users.

This supplementary issue of the Pakistan Journal of Urology contains diverse studies spanning organ donation's significance, surgical techniques, and the comparison of treatments in urology, but it doesn't provide specific research results or detailed findings.

This study found that specific human hair keratins are differentially expressed in the hair follicle, suggesting a shared pathway of epithelial differentiation between hair cortex and nail plate cells.

This review summarizes the current understanding of Wnt signaling in relation to its roles in tissue regeneration and cancer progression and discusses recent medical advancements targeting this pathway for therapeutic purposes.

Smart microneedles using advanced tech could improve psoriasis treatment.

This review discusses advances in forensic DNA profiling, highlighting new genetic markers and methods for identifying unknown individuals, but reports no new research findings.

This review discusses advancements and challenges in using 3D bioprinting for diabetic foot ulcer treatments, highlighting potential improvements and existing limitations in replicating skin architecture and clinical application.

This study used machine learning to identify FDA-approved drugs afatinib, neratinib, and zanubrutinib as potential KRASG12C inhibitors for resistant non-small-cell lung cancer, highlighting the potential of AI in drug repurposing.

Machine learning is effective in predicting gene functions and their relationships with diseases.

This study found that integrating machine learning enhances the predictive accuracy of forensic DNA phenotyping from low template DNA, achieving high accuracy for traits like eye color, although challenges remain for admixed populations and complex traits.

This review highlights that gut dysbiosis and bacterial extracellular vesicles are key factors in PCOS pathophysiology, and suggests AI-driven analysis of these profiles could enhance diagnostic accuracy and treatment personalization, though ethical concerns like data privacy and bias must be considered.

This review examines physical methods like electron paramagnetic resonance spectroscopy, Raman spectroscopy, and differential scanning calorimetry, highlighting their complementary capabilities in analyzing biomolecular structures and mycobacterial interactions with reactive oxygen species and antioxidants.